{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6df2b79-3029-4d40-912a-00c41d159203",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "raw_df=np.loadtxt('logi-y.txt',delimiter=',',encoding='utf-8')\n",
    "data=raw_df[:,0:2]\n",
    "target=raw_df[:,2]\n",
    "x,y=data,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1,test_size=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f599298-9b0d-470a-bea6-55c59a4c7f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测的准确率： 0.8666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "ac=accuracy_score(y_test,model.predict(x_test))\n",
    "print(\"模型预测的准确率：\",ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f01fb5c-4bdd-49ed-890f-cc797295b39d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "#绘制分类界面\n",
    "N,M=500,500\n",
    "t1=np.linspace(0,100,N)\n",
    "t2=np.linspace(0,100,M)\n",
    "x1,x2=np.meshgrid(t1,t2)\n",
    "x_new=np.stack((x1.flat,x2.flat),axis=1)\n",
    "y_predict=model.predict(x_new)\n",
    "y_hat=y_predict.reshape(x1.shape)\n",
    "iris_cmap=ListedColormap([\"#ACF080\",\"#A0A0FF\"])\n",
    "plt.pcolormesh(x1,x2,y_hat,cmap=iris_cmap)\n",
    "#绘制标签为0的样本点\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=60,c='g',marker='o')\n",
    "#绘制标签为1的样本点\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=60,c='r',marker='^')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.xlabel('科目一成绩')\n",
    "plt.ylabel('科目二成绩')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "665a6230-0b28-4196-8cff-cbea951381e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
